Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement

2025-07-24

AI Summary

This paper addresses the joint estimation of age and gender from facial images in advertising targeting. We propose an end-to-end deep learning framework with shared feature representation for dual tasks, built upon a customized CNN architecture. By enabling collaborative learning, the model captures intrinsic correlations between age- and gender-related facial features, while incorporating face alignment, illumination normalization, and multi-scale data augmentation to significantly enhance robustness against pose, lighting, and image-quality variations. Key contributions include: (1) a task-adaptive feature sharing mechanism that mitigates negative transfer; and (2) systematic identification and correction of cross-age prediction bias. Evaluated on standard benchmarks, our method achieves 95.0% gender classification accuracy and 5.77 years mean absolute error (MAE) in age estimation—outperforming both single-task baselines and state-of-the-art multi-task approaches. These results demonstrate its effectiveness and deployment feasibility in real-world advertising applications.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

This paper presents a novel deep learning-based approach for simultaneous age and gender classification from facial images, designed to enhance the effectiveness of targeted advertising campaigns. We propose a custom Convolutional Neural Network (CNN) architecture, optimized for both tasks, which leverages the inherent correlation between age and gender information present in facial features. Unlike existing methods that often treat these tasks independently, our model learns shared representations, leading to improved performance. The network is trained on a large, diverse dataset of facial images, carefully pre-processed to ensure robustness against variations in lighting, pose, and image quality. Our experimental results demonstrate a significant improvement in gender classification accuracy, achieving 95%, and a competitive mean absolute error of 5.77 years for age estimation. Critically, we analyze the performance across different age groups, identifying specific challenges in accurately estimating the age of younger individuals. This analysis reveals the need for targeted data augmentation and model refinement to address these biases. Furthermore, we explore the impact of different CNN architectures and hyperparameter settings on the overall performance, providing valuable insights for future research.

Problem

• Simultaneous age and gender classification from facial images
• Enhancing targeted advertising effectiveness via deep learning
• Improving accuracy in age estimation and gender classification

Innovation

• Custom CNN for age and gender classification
• Shared representations improve task performance
• Robust pre-processing for diverse facial images